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Hello, there newbie tech learners!
If you are still engulfed by the delusions and misconceptions, which are instilled into you by manipulators, then it’s time to use your boxed brain.
It was neither those expensive fancy boot camps nor those extremely expensive education platforms which created the world’s top programmers and data scientists.
It has always been those tips, minute details, and basics that a learner grasps in the beginning and is ultimately led towards his goal.
So, see what it takes to understand supervised learning and navigate your way in.


In simple words, supervised learning is a learning in which we teach or train the machine using data that is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples (data) so that the supervised learning algorithm analyzes the training data(set of training examples) and produces a correct outcome from labeled data.
Supervised learning is a machine learning task of learning a function. The function maps an input to an output based on example input-output pairs. In supervised learning, each example is a pair consisting of an input object and the desired output value.
In supervised learning, there exists a teacher or trainer. The trainer corrects the network’s response to a set of inputs. Pairs of inputs and outputs have to be presented to the network. The network takes each input and produces the corresponding output, which it then compares to the correct output. As a result, the network constructs an internal representation of inputs and outputs.


Supervised learning is the most common technique for training neural networks and decision trees. Both of these techniques are highly dependent on the information was given by the pre-determined classifications. In the case of neural networks, the classification is used to determine the error of the network and then adjust the network to minimize it, and in decision trees, the classifications are used to determine what attributes provide the most information that can be used to solve the classification puzzle.


There are two types of Supervised Learning techniques: Regression and Classification.

  1. Classification separates the data,
  2. Regression fits of the data.

During training, a classification algorithm will be given data points with an assigned category. The job of a classification algorithm is to then take an input value and assign it a class, or category, that it fits into based on the training data provided.
Classification problems can be solved with a whole lot of algorithms. Whichever algorithm you choose to use depends on the data and the situation. Here are a few popular classification algorithms:

  • Linear Classifiers
  • Support Vector Machines
  • Decision Trees
  • K-Nearest Neighbor
  • Random Forest

The most common example of classification is determining if an email is spam or not. With two classes to choose from (spam, or not spam), this problem is called a binary classification problem. The algorithm will be given training data with emails that are both spam and not spam.


Regression is a predictive statistical process where the model attempts to find the important relationship between dependent and independent variables. The goal of a regression algorithm is to predict a continuous number such as sales, income, and test scores. The equation for basic linear regression can be written as so:
Where x[i] is the feature(s) for the data and where w[i] and b are parameters which are developed during training. For simple linear regression models with only one feature in the data, the formula looks like this:
^y= wx+b
There are many different types of regression algorithms. The three most commonly named are listed below:

  • Linear Regression
  • Logistic Regression
  • Polynomial Regression

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.
Supervised learning is a sub-branch of machine learning. So like mentioned before Supervised learning, in the context of Machine learning, is a type of system in which both input and desired output data are provided. Input and output data are labeled for classification to provide a learning basis for future data processing.
Supervised learning is termed as the simplest subcategory of machine learning and serves as an introduction to machine learning to many machine learning practitioners. Supervised learning is the most commonly used form of machine learning, and has proven to be an excellent tool in many fields.


In supervised learning you have a set of labeled data, meaning that you have the values of the inputs and the outputs.  There are many different algorithms in machine learning that allow you to obtain a model of the data.


learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task.
Supervised machine learning systems provide the learning algorithms with known quantities to support future judgments.
In supervised learning, you train an algorithm and at the end of the process, you pick the function that best describes the input data, the one that for a given X makes the best estimation for Y.
In a nutshell, when training a supervised learning algorithm, the training data will consist of inputs paired with the correct outputs. During training, the algorithm will search for patterns in the data that correlate with the desired outputs. After training, a supervised learning algorithm will take in new unseen inputs and will determine which label the new inputs will be classified as based on prior training data. The objective of a supervised learning model is to predict the correct label for the newly presented input data.
You can say that a supervised learning algorithm learns from labeled training data and so helps you to predict outcomes for unforeseen data. It analyzes the training data and produces an inferred function, which can be used for mapping new examples.
A wide range of supervised learning algorithms is available, each with its strengths and weaknesses.
The most widely used learning algorithms are:

  • Support Vector Machines.
  • linear regression.
  • logistic regression.
  • naive Bayes.
  • linear discriminant analysis.
  • decision trees.
  • k-nearest neighbor algorithm.
  • Neural Networks (Multilayer perceptron)

Some popular examples of supervised machine learning algorithms are:

  • Linear regression for regression problems.
  • Random forest for classification and regression problems.
  • Support vector machines for classification problems.

Notice something important here: in the classification problem, the goal of the learning algorithm is to minimize the error with respect to the given inputs. These inputs, often called the “training set”, are the examples from which the agent tries to learn. But learning the training set well is not necessarily the best thing to do.

  • Systems Biology – Gene expression microarray data:

You can separate malignant from healthy tissues based on the mRNA expression profile of the tissue.

  • Text categorization: spam detection

Categorize text documents into predefined categories. For example, categorize news into ‘sports’, ‘politics’, ‘science’, etc.

  • Face detection:

By supervised learning discriminating human faces from non-faces is now made possible, such as in smartphones and cameras.

  • Signature recognition:

Recognize signatures by structural similarities that are difficult to quantify.

  • Medicine:

Predict if a patient has heart ischemia by spectral analysis of his/her ECG.

  • Customer discovery:

In customer discovery, you predict whether a customer is likely to purchase certain goods according to a database of customer profiles and their history of shopping activities.

  • Character recognition:

Character recognition or digit recognition helps you to identify handwritten characters, for instance, classifying each image of character into one of 10 categories ‘0’, ‘1’, ‘2’.

  • Speech recognition:

Speech recognition using hidden Markov models and Bayesian networks rely on some elements of supervision as well in order to adjust parameters to, as usual, minimize the error on the given inputs.

  • You get a bunch of photos with information about what is on them and then you train a model to recognize new photos.
  • You have a bunch of molecules and information about which are drugs and you train a model to answer whether a new molecule is also a drug.
  • classifying whether a patient has a disease or not
  • classifying whether an email is spam or not
  • Cortana or any speech automated system on your mobile phone trains your voice and then starts working based on this training.
  • Based on various features (past record of head-to-head, pitch, toss, player-vs-player) WASPpredicts the winning % of both teams.
  • Train your handwriting to OCR system and once trained, it will be able to convert your hand-writing images into text (till some accuracy obviously)
  • Based on some prior knowledge (when its sunny, temperature is higher; when its cloudy, humidity is higher, etc.) weather apps predict the parameters for a given time.
  • Based on past information about spams, filtering out a new incoming email into Inbox(normal) or Junk folder (Spam)
  • Biometric attendance or ATM etc systems where you train the machine after a couple of inputs (of your biometric identity – be it thumb or iris or ear-lobe, etc.), the machine can validate your future input and identify you.
  • You have data on the stock market which is of previous data and to get results of the present input for the next few years by giving some instructions it can give you needed output.

Semisupervised learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training; generally a small amount of labeled data with a large amount of unlabeled data.
In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. Typically, this combination will contain a very small amount of labeled data and a very large amount of unlabeled data. The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabeled data.
A Semi-Supervised algorithm works with the following assumptions about the data :

  1. Continuity Assumption:The algorithm assumes that the points which are closer to each other are more likely to have the same output label.
  2. Cluster Assumption: The data can be divided into discrete clusters and points in the same cluster are more likely to share an output label.
  3. Manifold Assumption: This assumption allows the use of distances and densities which are defined on a manifold.

In 2016 Google launched a new Semi-Supervised learning tool called Google Expander too.
Here are some practical applications of Semi-Supervised Learning :

  • Speech Analysis
  • Internet Content Classification
  • Protein Sequence Classification

Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output.
Supervised learning techniques have achieved great success when there is strong supervision information like a large number of training examples with ground-truth labels.
This is a brief guide for your starters, so stop making excuses for expensive boot camps, a simple guide may guide you enough to start your dig.

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